Sex dependent risk factors for mortality after myocardial infarction

Individual patient data meta-analysis

H.M. van Loo, E.R. van den Heuvel, R.A. Schroevers, M. Anselmino, R.M. Carney, J. Denollet, F. Doyle, K.E. Freedland, S.L. Grace, S.H. Hosseini, Kapil Parakh, L. Pilote, C. Rafanelli, A.M. Roest, H. Sato, R.P. Steeds, R.C. Kessler, P. de Jonge

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Abstract

Background
Although a number of risk factors are known to predict mortality within the first years after myocardial infarction, little is known about interactions between risk factors, whereas these could contribute to accurate differentiation of patients with higher and lower risk for mortality. This study explored the effect of interactions of risk factors on all-cause mortality in patients with myocardial infarction based on individual patient data meta-analysis.
Methods
Prospective data for 10,512 patients hospitalized for myocardial infarction were derived from 16 observational studies (MINDMAPS). Baseline measures included a broad set of risk factors for mortality such as age, sex, heart failure, diabetes, depression, and smoking. All two-way and three-way interactions of these risk factors were included in Lasso regression analyses to predict time-to-event related all-cause mortality. The effect of selected interactions was investigated with multilevel Cox regression models.
Results
Lasso regression selected five two-way interactions, of which four included sex. The addition of these interactions to multilevel Cox models suggested differential risk patterns for males and females. Younger women (age <50) had a higher risk for all-cause mortality than men in the same age group (HR 0.7 vs. 0.4), while men had a higher risk than women if they had depression (HR 1.4 vs. 1.1) or a low left ventricular ejection fraction (HR 1.7 vs. 1.3). Predictive accuracy of the Cox model was better for men than for women (area under the curves: 0.770 vs. 0.754).
Conclusions
Interactions of well-known risk factors for all-cause mortality after myocardial infarction suggested important sex differences. This study gives rise to a further exploration of prediction models to improve risk assessment for men and women after myocardial infarction.Keywords: All-cause mortality, Interactions, Myocardial infarction, Prediction, Risk factors, Sex
Original languageEnglish
Article number242
JournalBMC Medicine
Volume12
DOIs
Publication statusPublished - 2014

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Proportional Hazards Models
Depression
Area Under Curve
Age Groups

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van Loo, H. M., van den Heuvel, E. R., Schroevers, R. A., Anselmino, M., Carney, R. M., Denollet, J., ... de Jonge, P. (2014). Sex dependent risk factors for mortality after myocardial infarction: Individual patient data meta-analysis. BMC Medicine, 12, [242]. https://doi.org/10.1186/s12916-014-0242-y
van Loo, H.M. ; van den Heuvel, E.R. ; Schroevers, R.A. ; Anselmino, M. ; Carney, R.M. ; Denollet, J. ; Doyle, F. ; Freedland, K.E. ; Grace, S.L. ; Hosseini, S.H. ; Parakh, Kapil ; Pilote, L. ; Rafanelli, C. ; Roest, A.M. ; Sato, H. ; Steeds, R.P. ; Kessler, R.C. ; de Jonge, P. / Sex dependent risk factors for mortality after myocardial infarction : Individual patient data meta-analysis. In: BMC Medicine. 2014 ; Vol. 12.
@article{6917f1c59681450890625d1ee83853c5,
title = "Sex dependent risk factors for mortality after myocardial infarction: Individual patient data meta-analysis",
abstract = "BackgroundAlthough a number of risk factors are known to predict mortality within the first years after myocardial infarction, little is known about interactions between risk factors, whereas these could contribute to accurate differentiation of patients with higher and lower risk for mortality. This study explored the effect of interactions of risk factors on all-cause mortality in patients with myocardial infarction based on individual patient data meta-analysis.MethodsProspective data for 10,512 patients hospitalized for myocardial infarction were derived from 16 observational studies (MINDMAPS). Baseline measures included a broad set of risk factors for mortality such as age, sex, heart failure, diabetes, depression, and smoking. All two-way and three-way interactions of these risk factors were included in Lasso regression analyses to predict time-to-event related all-cause mortality. The effect of selected interactions was investigated with multilevel Cox regression models.ResultsLasso regression selected five two-way interactions, of which four included sex. The addition of these interactions to multilevel Cox models suggested differential risk patterns for males and females. Younger women (age <50) had a higher risk for all-cause mortality than men in the same age group (HR 0.7 vs. 0.4), while men had a higher risk than women if they had depression (HR 1.4 vs. 1.1) or a low left ventricular ejection fraction (HR 1.7 vs. 1.3). Predictive accuracy of the Cox model was better for men than for women (area under the curves: 0.770 vs. 0.754).ConclusionsInteractions of well-known risk factors for all-cause mortality after myocardial infarction suggested important sex differences. This study gives rise to a further exploration of prediction models to improve risk assessment for men and women after myocardial infarction.Keywords: All-cause mortality, Interactions, Myocardial infarction, Prediction, Risk factors, Sex",
author = "{van Loo}, H.M. and {van den Heuvel}, E.R. and R.A. Schroevers and M. Anselmino and R.M. Carney and J. Denollet and F. Doyle and K.E. Freedland and S.L. Grace and S.H. Hosseini and Kapil Parakh and L. Pilote and C. Rafanelli and A.M. Roest and H. Sato and R.P. Steeds and R.C. Kessler and {de Jonge}, P.",
year = "2014",
doi = "10.1186/s12916-014-0242-y",
language = "English",
volume = "12",
journal = "BMC Medicine",
issn = "1741-7015",
publisher = "BioMed Central",

}

van Loo, HM, van den Heuvel, ER, Schroevers, RA, Anselmino, M, Carney, RM, Denollet, J, Doyle, F, Freedland, KE, Grace, SL, Hosseini, SH, Parakh, K, Pilote, L, Rafanelli, C, Roest, AM, Sato, H, Steeds, RP, Kessler, RC & de Jonge, P 2014, 'Sex dependent risk factors for mortality after myocardial infarction: Individual patient data meta-analysis', BMC Medicine, vol. 12, 242. https://doi.org/10.1186/s12916-014-0242-y

Sex dependent risk factors for mortality after myocardial infarction : Individual patient data meta-analysis. / van Loo, H.M.; van den Heuvel, E.R.; Schroevers, R.A.; Anselmino, M.; Carney, R.M.; Denollet, J.; Doyle, F.; Freedland, K.E.; Grace, S.L.; Hosseini, S.H.; Parakh, Kapil; Pilote, L.; Rafanelli, C.; Roest, A.M.; Sato, H.; Steeds, R.P.; Kessler, R.C.; de Jonge, P.

In: BMC Medicine, Vol. 12, 242, 2014.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Sex dependent risk factors for mortality after myocardial infarction

T2 - Individual patient data meta-analysis

AU - van Loo, H.M.

AU - van den Heuvel, E.R.

AU - Schroevers, R.A.

AU - Anselmino, M.

AU - Carney, R.M.

AU - Denollet, J.

AU - Doyle, F.

AU - Freedland, K.E.

AU - Grace, S.L.

AU - Hosseini, S.H.

AU - Parakh, Kapil

AU - Pilote, L.

AU - Rafanelli, C.

AU - Roest, A.M.

AU - Sato, H.

AU - Steeds, R.P.

AU - Kessler, R.C.

AU - de Jonge, P.

PY - 2014

Y1 - 2014

N2 - BackgroundAlthough a number of risk factors are known to predict mortality within the first years after myocardial infarction, little is known about interactions between risk factors, whereas these could contribute to accurate differentiation of patients with higher and lower risk for mortality. This study explored the effect of interactions of risk factors on all-cause mortality in patients with myocardial infarction based on individual patient data meta-analysis.MethodsProspective data for 10,512 patients hospitalized for myocardial infarction were derived from 16 observational studies (MINDMAPS). Baseline measures included a broad set of risk factors for mortality such as age, sex, heart failure, diabetes, depression, and smoking. All two-way and three-way interactions of these risk factors were included in Lasso regression analyses to predict time-to-event related all-cause mortality. The effect of selected interactions was investigated with multilevel Cox regression models.ResultsLasso regression selected five two-way interactions, of which four included sex. The addition of these interactions to multilevel Cox models suggested differential risk patterns for males and females. Younger women (age <50) had a higher risk for all-cause mortality than men in the same age group (HR 0.7 vs. 0.4), while men had a higher risk than women if they had depression (HR 1.4 vs. 1.1) or a low left ventricular ejection fraction (HR 1.7 vs. 1.3). Predictive accuracy of the Cox model was better for men than for women (area under the curves: 0.770 vs. 0.754).ConclusionsInteractions of well-known risk factors for all-cause mortality after myocardial infarction suggested important sex differences. This study gives rise to a further exploration of prediction models to improve risk assessment for men and women after myocardial infarction.Keywords: All-cause mortality, Interactions, Myocardial infarction, Prediction, Risk factors, Sex

AB - BackgroundAlthough a number of risk factors are known to predict mortality within the first years after myocardial infarction, little is known about interactions between risk factors, whereas these could contribute to accurate differentiation of patients with higher and lower risk for mortality. This study explored the effect of interactions of risk factors on all-cause mortality in patients with myocardial infarction based on individual patient data meta-analysis.MethodsProspective data for 10,512 patients hospitalized for myocardial infarction were derived from 16 observational studies (MINDMAPS). Baseline measures included a broad set of risk factors for mortality such as age, sex, heart failure, diabetes, depression, and smoking. All two-way and three-way interactions of these risk factors were included in Lasso regression analyses to predict time-to-event related all-cause mortality. The effect of selected interactions was investigated with multilevel Cox regression models.ResultsLasso regression selected five two-way interactions, of which four included sex. The addition of these interactions to multilevel Cox models suggested differential risk patterns for males and females. Younger women (age <50) had a higher risk for all-cause mortality than men in the same age group (HR 0.7 vs. 0.4), while men had a higher risk than women if they had depression (HR 1.4 vs. 1.1) or a low left ventricular ejection fraction (HR 1.7 vs. 1.3). Predictive accuracy of the Cox model was better for men than for women (area under the curves: 0.770 vs. 0.754).ConclusionsInteractions of well-known risk factors for all-cause mortality after myocardial infarction suggested important sex differences. This study gives rise to a further exploration of prediction models to improve risk assessment for men and women after myocardial infarction.Keywords: All-cause mortality, Interactions, Myocardial infarction, Prediction, Risk factors, Sex

U2 - 10.1186/s12916-014-0242-y

DO - 10.1186/s12916-014-0242-y

M3 - Article

VL - 12

JO - BMC Medicine

JF - BMC Medicine

SN - 1741-7015

M1 - 242

ER -